Classifying Distributions via Symbolic Entropy Estimation

ResearchSpace/Manakin Repository

Show simple item record Speidel, Ulrich en
dc.coverage.spatial Singapore en 2018-10-01T23:48:47Z en 2015-12-02 en
dc.identifier.uri en
dc.description.abstract Shannon observed that the normal distribution has maximal entropy among distributions with a density function and a given variance. This sparked a significant body of research in statistics, broadly concerned with goodness-of-fit estimators based on Shannon entropy for a variety of distributions and, in particular, normality testing. The present paper proposes to use compression algorithms and other parsing-based entropy estimators to match samples in sampling order to one of a set of distributions with the observed and, where applicable, , using the distributions’ quantile functions to convert the samples into a string of symbols for entropy estimation. The paper demonstrates with a series of Monte-Carlo simulations that the proposed technique may be able to distinguish between a number of common distributions even if the samples themselves are not i.i.d. en
dc.relation.ispartof 10th International Conference on Information, Communications and Signal Processing en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri en
dc.title Classifying Distributions via Symbolic Entropy Estimation en
dc.type Conference Item en
dc.rights.holder Copyright: The author en en
pubs.finish-date 2015-12-04 en
pubs.start-date 2015-12-02 en
dc.rights.accessrights en
pubs.subtype Proceedings en
pubs.elements-id 504642 en Science en School of Computer Science en
pubs.record-created-at-source-date 2015-11-12 en

Full text options

Find Full text

This item appears in the following Collection(s)

Show simple item record


Search ResearchSpace

Advanced Search